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Mid-level ML platform role with niche LLM skills and a known startup brand, moderate competition.
Highly domain-specific ML platform and LLM experience required, limiting cross-industry transferability.
Requires 5+ years plus mandatory MLOps, LLM gateway, and cloud/container skills.
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Own and operate the LLM control plane and gateway powering multi-tenant, real-time AI applications in automotive retail with SLA, cost, and safety guardrails.
Develop and maintain APIs, orchestration patterns, agent runtimes, and platform components for both large language models and classical ML pipelines operationalizing dealership data.
Define standards for evaluation, safety, deployment, and governance to enable fast, reliable delivery of AI-powered features improving dealer KPIs like upsell, cycle time, and CSAT.
Minimum 5 years experience building large-scale data, machine learning, or platform systems with strong software engineering fundamentals.
Proficiency in Python plus Java/Scala/Go; experience with microservices and API design.
Experience with MLOps tooling and infrastructure at scale, including CI/CD, tracking/registry, A/B testing, and streaming data processing.
Familiarity with cloud (AWS preferred), container orchestration (Docker/Kubernetes), and production-grade system design (cost, latency, multi-tenant SaaS).
Experienced in building and operating large-scale LLM gateways/control planes with features like dynamic routing, quota management, caching, and cost accounting.
Skilled in agentic systems design including tool integrations, orchestration, human-in-the-loop safety workflows, and telemetry evaluation.
Strong background in knowledge graphs, graph query languages, and hybrid retrieval methods combining vector and keyword search optimized for real-time accuracy and cost.